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Dimitris Fotakis, Panagiotis Patsilinakos, Eleni Psaroudaki and Michalis Xefteris
In this work, we consider the problem of shape-based time-series clustering with the widely used Dynamic Time Warping (DTW) distance. We present a novel two-stage framework based on Sparse Gaussian Modeling. In the first stage, we apply Sparse Gaussian P...
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Mizuki Asano, Takumi Miyoshi and Taku Yamazaki
Smart home environments, which consist of various Internet of Things (IoT) devices to support and improve our daily lives, are expected to be widely adopted in the near future. Owing to a lack of awareness regarding the risks associated with IoT devices ...
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Renjie Chen and Nalini Ravishanker
With the advancement of IoT technologies, there is a large amount of data available from wireless sensor networks (WSN), particularly for studying climate change. Clustering long and noisy time series has become an important research area for analyzing t...
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Massimo Pacella, Matteo Mangini and Gabriele Papadia
Considering the issue of energy consumption reduction in industrial plants, we investigated a clustering method for mining the time-series data related to energy consumption. The industrial case study considered in our work is one of the most energy-inte...
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Carla Sahori Seefoo Jarquin, Alessandro Gandelli, Francesco Grimaccia and Marco Mussetta
Understanding how, why and when energy consumption changes provides a tool for decision makers throughout the power networks. Thus, energy forecasting provides a great service. This research proposes a probabilistic approach to capture the five inherent ...
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D. Criado-Ramón, L. G. B. Ruiz and M. C. Pegalajar
Pattern sequence-based models are a type of forecasting algorithm that utilizes clustering and other techniques to produce easily interpretable predictions faster than traditional machine learning models. This research focuses on their application in ene...
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Peng-Yeng Yin
Air pollution has been a global issue that solicits proposals for sustainable development of social economics. Though the sources emitting pollutants are thoroughly investigated, the transportation, dispersion, scattering, and diminishing of pollutants i...
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Zhiguo Liang, Lijun Zhang and Xizhe Wang
Since failure of steam turbines occurs frequently and can causes huge losses for thermal plants, it is important to identify a fault in advance. A novel clustering fault diagnosis method for steam turbines based on t-distribution stochastic neighborhood ...
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Dhan Lord B. Fortela, Ashton C. Fremin, Wayne Sharp, Ashley P. Mikolajczyk, Emmanuel Revellame, William Holmes, Rafael Hernandez and Mark Zappi
This work focused on demonstrating the capability of unsupervised machine learning techniques in detecting impending anomalies by extracting hidden trends in the datasets of fuel economy and emissions of light-duty vehicles (LDVs), which consist of cars ...
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Timothy Tadj, Reza Arablouei and Volkan Dedeoglu
Data trust in IoT is crucial for safeguarding privacy, security, reliable decision-making, user acceptance, and complying with regulations. Various approaches based on supervised or unsupervised machine learning (ML) have recently been proposed for evalu...
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